CN112116249A

CN112116249A - Traffic information processing method and electronic equipment

Info

Publication number: CN112116249A
Application number: CN202010987448.2A
Authority: CN
Inventors: 宫庆胜; 孔涛; 张涛; 陈罗刚; 聂增国; 史晓燕
Original assignee: Hisense TransTech Co Ltd
Current assignee: Hisense TransTech Co Ltd
Priority date: 2020-09-18
Filing date: 2020-09-18
Publication date: 2020-12-22
Anticipated expiration: 2040-09-18
Also published as: CN112116249B

Abstract

The embodiment of the application provides a traffic information processing method and electronic equipment, along with the development of economy, the urbanization process is accelerated, the quantity of motor vehicles is rapidly increased, and the problem of urban traffic jam is increasingly highlighted. The reasons for traffic jam are complex and various, the representation forms of traffic problems are different, and the optimization strategy, the national standard and the application cases are also various. Manually analyzing such information is necessarily inefficient and requires strict knowledge of personnel. According to the method provided by the application, after the traffic problem is positioned, the corresponding handling strategy is matched according to the traffic problem, and the national standard and the application case related to the handling strategy are provided and output to the user for displaying, so that the user can obtain some valuable reference information to solve the congestion problem, a detailed implementation scheme can be well understood according to the handling strategy, and the traffic congestion can be accurately and quickly relieved.

Description

Traffic information processing method and electronic equipment

Technical Field

The present disclosure relates to the field of intelligent traffic control and traffic signal optimization, and in particular, to a traffic information processing method and an electronic device.

Background

With the development of economy and the acceleration of urbanization process, the quantity of motor vehicles is rapidly increased, and the problem of urban traffic jam is increasingly highlighted. The reasons for traffic jam are complex and various, the representation forms of traffic problems are different, and the optimization strategy, the national standard and the application cases are also various.

When the traffic is congested, experts manually analyze and make strategies in the related technologies. However, this approach is inefficient and requires a simple and quick approach to solving the traffic congestion problem.

Disclosure of Invention

The application aims to provide a traffic information processing method and electronic equipment, which are used for solving the following problems: the traffic problem is correctly identified according to the problem characterization phenomenon, an optimization strategy is further provided in a targeted manner, and the optimization strategy is implemented according to the requirements of national standard standards to relieve traffic jam.

The embodiment of the application provides a traffic information processing method, which comprises the following steps:

acquiring a target traffic problem;

acquiring a target disposal strategy corresponding to the target traffic problem according to a corresponding relation between the traffic problem and the disposal strategy which is established in advance;

searching a national standard and an application case associated with the target disposal strategy;

and outputting and displaying the target disposal strategy and the national standard specification and the application case associated with the target disposal strategy.

In one embodiment, the obtaining the target traffic problem includes:

after the congestion phenomenon features are obtained, matching processing is carried out in a traffic question bank according to the congestion phenomenon features, and traffic questions matched with the congestion phenomenon features are obtained and serve as the target traffic questions; or the like, or, alternatively,

and responding to the input operation of the traffic problem to obtain the target traffic problem.

In one embodiment, the performing matching processing in a traffic question bank according to the congestion phenomenon feature to obtain a traffic question matched with the congestion phenomenon feature includes:

obtaining predetermined degrees of correlation between the congestion phenomenon characteristics and a plurality of traffic problems;

the plurality of traffic problems are ranked according to the relevancy, and a plurality of candidate traffic problems are output according to a ranking result;

and in response to the selection operation of the candidate traffic question, taking the selected candidate traffic question as the traffic question matched with the congestion phenomenon feature.

In one embodiment, the method further comprises:

determining the relevance of the congestion phenomenon characteristics and a plurality of traffic problems according to a matching degree calculation formula:

the matching degree calculation formula is as follows:

wherein n is the number of the candidate traffic problems, and each candidate traffic problem corresponds to multiple congestion phenomenon characteristics; i denotes the ith question, x_ijA quantized value representing the j congestion phenomenon feature of the ith question, wherein the congestion phenomenon feature of each traffic question in the traffic question bank is y_i，y_i0 means that the congestion phenomenon feature cannot be matched to the traffic problem in the traffic problem bank, y_i1 indicates that the congestion phenomenon feature can be matched to the traffic problem in the traffic problem bank,

represents the average of the n traffic problems over the jth congestion feature,

and representing the average value of the n traffic problems in all the congestion phenomenon characteristic values.

In one embodiment, in response to a selection operation of a candidate traffic question, after the selected candidate traffic question is taken as a traffic question matching the congestion phenomenon feature, the method further includes:

and if the correlation degree between the selected candidate traffic problem and the congestion phenomenon feature is not the maximum value in the plurality of candidate traffic problems, improving the correlation degree between the selected candidate traffic problem and the congestion phenomenon feature.

determining the matching degree between the congestion phenomenon characteristics and each candidate traffic problem in a traffic problem library through a matching degree calculation formula;

the matching degree calculation formula is as follows:

wherein n is the number of the candidate traffic problems, and each candidate problem corresponds to multiple congestion phenomenon characteristics; i denotes the ith question, x_ijA jth congestion feature representing an ith question, the congestion of each traffic question in the traffic question bank characterized by y_i，y_i0 means that the congestion phenomenon feature cannot be matched to the traffic problem in the traffic problem bank, y_i1 indicates that the congestion phenomenon feature can be matched to the traffic problem in the traffic problem bank,

an average of the congestion phenomenon characteristics representing the traffic problem;

and selecting the matched traffic question from a traffic question library as the target traffic question according to the matching degree.

In one embodiment, after the matching process is performed in a traffic question bank according to the congestion phenomenon feature to obtain the traffic question matched with the congestion phenomenon feature, the method further includes:

outputting a prompt for confirming whether to adopt the target traffic problem;

if the first user operation for confirming the adoption of the target traffic problem is detected, executing the step of acquiring a target disposal strategy corresponding to the target traffic problem according to the corresponding relation between the pre-established traffic problem and the disposal strategy;

if a second user operation confirming that the target traffic problem is not adopted is detected, any one of the following operations is executed:

operation 1: acquiring new congestion problem characteristics, and returning to execute the step of matching processing in a traffic problem library according to the congestion phenomenon characteristics;

operation 2: and acquiring a new target traffic problem matched with the congestion phenomenon characteristics, and returning to execute the step of outputting a prompt for confirming whether the target traffic problem is adopted.

In one embodiment, after the obtaining of the target handling policy corresponding to the target traffic problem according to the pre-established correspondence between the traffic problem and the handling policy, the method further includes:

outputting a prompt to confirm whether the target handling strategy can alleviate the target traffic issue;

if a third user operation confirming that the target handling strategy can relieve the target traffic problem is detected, the operation of searching the national standard and the application case associated with the target handling strategy is executed;

if a fourth user operation is detected that confirms that the target handling strategy cannot alleviate the target traffic issue, performing any of the following operations:

operation 3: acquiring new congestion phenomenon characteristics, and returning to execute the step of performing matching processing in a traffic question bank according to the congestion phenomenon characteristics;

and operation 4: and acquiring a new target handling strategy corresponding to the target traffic problem, and returning to execute the step of outputting a prompt for confirming whether the target handling strategy can relieve the target traffic problem.

In one embodiment, after the performing the operation of finding the national standard specification and the application case associated with the target disposal policy, the method further comprises:

outputting a prompt to confirm whether the target handling policy is reasonable;

if a fifth user operation confirming that the target handling policy is reasonable is detected, executing an operation of applying the target handling policy;

if a sixth user action confirming that the target handling policy is unreasonable is detected, performing any one of the following actions:

operation 5: acquiring new congestion problem characteristics, and returning to execute the step of matching processing in a traffic problem library according to the congestion phenomenon characteristics;

operation 6: and acquiring a new target handling strategy corresponding to the target traffic problem, and returning to execute the step of outputting a prompt for confirming whether the target handling strategy can relieve the target traffic problem.

In one embodiment, after the displaying the target disposal policy and the national standard specification associated therewith and the application case output, the method further comprises:

outputting a prompt to confirm whether the target traffic issue is resolved with the target handling strategy;

if a seventh user operation confirming that the target traffic problem can be solved is detected, acquiring an input optimization strategy aiming at a target disposal strategy, and adding an incidence relation between the optimization strategy and the target traffic into the corresponding relation;

if detecting an eighth user operation confirming that the target traffic problem cannot be solved; then any of the following operations are performed:

operation 7: acquiring new congestion problem characteristics, and returning to execute the step of matching processing in a traffic problem library according to the congestion phenomenon characteristics;

operation 8: and acquiring a new target handling strategy corresponding to the target traffic problem, and returning to execute the step of outputting a prompt for confirming whether the target handling strategy can relieve the target traffic problem.

In one embodiment, after the establishing the association between the optimization strategy and the target traffic, the method further comprises:

and when a target handling strategy corresponding to the target traffic problem is searched and obtained through the corresponding relation next time, preferentially recommending the target handling strategy and the optimization strategy.

Compared with the related technology, the traffic information processing method of the whole process is established, comprehensive and systematic assistance is provided for signal engineers, optimization strategies are guided to be implemented on the ground, and blockage relieving measures are accurate and efficient.

An embodiment of the present application further provides an electronic device for processing traffic information, where the electronic device includes a processor and a memory:

the memory for storing a computer program executable by the processor;

the processor is coupled to the memory and configured to:

acquiring a target traffic problem;

In one embodiment, the obtaining the target traffic problem, the processor is configured to:

In one embodiment, when performing the matching process in the traffic question bank according to the congestion phenomenon feature to obtain the traffic question matched with the congestion phenomenon feature, the processor is configured to:

In one embodiment, the processor is configured to:

the matching degree calculation formula is as follows:

In one embodiment, in response to a selection operation of a candidate traffic problem, after treating the selected candidate traffic problem as a traffic problem matching the congestion phenomenon feature, the processor is configured to:

In one embodiment, the matching process is performed in a traffic question bank according to the congestion phenomenon feature, so as to obtain the traffic question matched with the congestion phenomenon feature, and the processor is configured to:

the matching degree calculation formula is as follows:

In one embodiment, after the matching process is performed in a traffic question bank according to the congestion phenomenon feature to obtain the traffic question matched with the congestion phenomenon feature, the processor is further configured to:

outputting a prompt for confirming whether to adopt the target traffic problem;

In one embodiment, after obtaining the target treatment policy corresponding to the target traffic problem according to the pre-established correspondence between the traffic problem and the treatment policy, the processor is further configured to:

In one embodiment, after the performing the operation of finding the national standard specification and the application case associated with the target disposal policy, the processor is further configured to:

In one embodiment, after the displaying the target disposition policy and its associated national standard specification and the application case output, the processor is further configured to:

In one embodiment, after the establishing the association between the optimization strategy and the target traffic, the processor is further configured to:

Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments or related technologies of the present application, the drawings needed to be used in the description of the embodiments or related technologies are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

Fig. 1 is an application scenario diagram of a traffic information processing method according to an embodiment of the present application;

fig. 2 is a schematic view of a full-flow traffic optimization knowledge system of a traffic information processing method according to an embodiment of the present application;

fig. 3 is a flowchart in an implementation process of a traffic information processing method according to an embodiment of the present application;

fig. 4 is a schematic diagram of obtaining a traffic problem matched with a congestion phenomenon feature as a target traffic problem by way of the method 1 according to the embodiment of the present application;

FIG. 5 is a display interface for recommending traffic problems according to an embodiment of the present application;

FIG. 6 is a display interface for recommending traffic problems according to an embodiment of the present application;

fig. 7 is a schematic diagram of obtaining a traffic problem matched with a congestion phenomenon feature as a target traffic problem in manner 2 according to an embodiment of the present application;

FIG. 8 is a schematic diagram of determining whether to use a target traffic problem according to an embodiment of the present disclosure;

FIG. 9 is an interface for outputting a prompt for confirming whether to use a target traffic problem according to an embodiment of the present disclosure;

FIG. 10 is a schematic diagram of obtaining new congestion problem features according to an embodiment of the present application;

FIG. 11 is a schematic diagram of an engineer entering new congestion problem features in an input box according to an embodiment of the present application;

fig. 12 is a schematic diagram of determining whether a target handling strategy can alleviate the target traffic problem according to an embodiment of the present application;

fig. 13 is a schematic diagram of determining whether a target handling policy is reasonable according to an embodiment of the present application;

FIG. 14 is a schematic diagram of a target disposal policy and optimization policy input box in a policy optimization interface according to an embodiment of the present application;

fig. 15 is a schematic diagram of determining whether to solve a target traffic problem by using a target handling strategy according to an embodiment of the present application;

FIG. 16 is a schematic diagram of a disposal policy recommendation provided by an embodiment of the present application;

fig. 17 is a schematic view of an overall flow of a traffic information processing method according to an embodiment of the present application;

FIG. 18 is a schematic structural diagram of an apparatus according to an embodiment of the present disclosure;

fig. 19 is a schematic view of an electronic device according to an embodiment of the present application.

Detailed Description

In the embodiment of the present application, the term "and/or" describes an association relationship of associated objects, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.

In the embodiments of the present application, the term "plurality" means two or more, and other terms are similar thereto.

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The inventor researches and discovers that with the development of economy and the acceleration of urbanization process, the quantity of motor vehicles is rapidly increased, and the problem of urban traffic jam is increasingly highlighted. The reasons for traffic jam are complex and various, the representation forms of traffic problems are different, and the optimization strategy, the national standard and the application cases are also various. Manually analyzing such information is necessarily inefficient and requires strict knowledge of personnel. Therefore, establishing a quick and efficient optimization strategy recommendation knowledge system is an important module for traffic signal optimization and also an important embodiment of traffic organization refinement and signal timing intelligence. How to provide an optimization strategy according to the traffic problem causing congestion can be automatically and intelligently targeted, and further the optimization strategy can be implemented according to the requirements of national standard standards to relieve the traffic congestion, so that the method is the key and difficult work in the traffic industry.

In view of this, the present application provides a traffic information processing method for the purpose of relieving urban traffic congestion, and the main inventive concepts of the present application are as follows: after the traffic problem is positioned, the corresponding disposal strategy is matched based on a pre-established knowledge system, and a plurality of national standard specifications and application cases related to the disposal strategy are provided as far as possible and output to a user for display, so that the user can obtain a plurality of valuable reference information to solve the congestion problem, for example, when the strategy is disposed in real time, the relevant international specifications and application cases can be referred to so as to comprehensively know the disposal strategy to the applicable condition, so that the detailed implementation scheme can be better understood according to the disposal strategy, and the congestion problem can be accurately and quickly solved.

A traffic information processing method in the embodiments of the present application is described in detail below with reference to the drawings.

As shown in fig. 1, an application scenario diagram of a traffic information processing method provided in an embodiment of the present application is shown, where the application scenario diagram includes:

vehicle 101, road segment 102;

the quantity of motor vehicles is rapidly increased, so that the urban traffic jam problem is increasingly highlighted, as shown in fig. 1, further, the embodiment of the application provides a method for identifying the traffic problem correctly, further providing an optimization strategy in a targeted manner, and implementing the optimization strategy according to the requirements of national standard specifications to relieve the traffic jam.

Before the following steps are implemented, a full-flow traffic optimization knowledge system based on a knowledge graph structure is built, as shown in fig. 2, the knowledge system comprises: the system comprises a knowledge base layer, a knowledge management layer, a knowledge matching layer and a knowledge application layer. Wherein:

the knowledge base layer obtains congestion problem characteristics and traffic problems through a public transport opinion and routing inspection mode to form a phenomenon representation library and a traffic problem library. The public opinion information source comprises 12345 hot lines, captain mailboxes, blogs, instant messaging tools, traffic police dispatch modes and the like, and the patrol information comprises intersection patrol and road section patrol. The knowledge base layer is the basis of the knowledge matching layer and the knowledge application layer;

the knowledge management layer extracts keywords, sentences and paragraphs from the problem features of the knowledge base layer and the traffic problem information, and professionally reads different problem features and converts the problem features into traffic problem terms to form the problem keyword, sentence and paragraph units which are consistently recognized by the traffic industry. Meanwhile, with the development of intelligent traffic and the application of new technologies (internet, multi-target radar and the like), new problem characteristics, traffic problems and typical cases are gradually presented, relevant national standard specifications are gradually perfected, and engineers can selectively and periodically update the content of a knowledge management layer and complete a knowledge system in time.

The knowledge matching layer is a core module of the whole knowledge system, carries out rule matching on problem characteristics, traffic problems, optimization strategies, national standard specifications and application cases by setting knowledge association and strategy recommendation rules, recommends the optimization strategies by utilizing knowledge retrieval and knowledge reasoning, ensures that the actual traffic problems are really and effectively solved in a knowledge base, and really realizes the following and well-documented effects; the layer includes: a problem feature library, a traffic question library, a strategy recommendation library, a national standard library and an application case library; wherein:

the problem feature library has traffic problem recording features, and corresponding feature expressions are provided for different types of traffic objects in the problem feature library. The method comprises the following steps:

1. the traffic object is a motor vehicle, and the corresponding characteristic expression comprises the following steps: crossing overflow, crossing deadlock, crossing knotting, long crossing waiting time, long crossing queuing length, more parking times, short green light time, long red light time, right turn and pedestrian conflict, long right turn queuing, small crossing turning radius, unclear sign, invisible sign, undersized speed limit, inconsistent signal light and sign, inconsistent sign and marking, too narrow lane width, disorderly parking, motor vehicle jamming, lane changing, solid line pressing, red light running, reverse running and the like;

2. the traffic object is a non-motor vehicle: the interference between the vehicle and the non-motor vehicle is serious, no non-motor vehicle lane exists, the right-turning vehicle and the non-motor vehicle conflict, the non-motor vehicle runs the red light, and the like;

3. the traffic object is a pedestrian: pedestrians cannot cross the street, the green light time of the pedestrians is short, the red light time is long, the right turn collides with the pedestrians, the pedestrians run the red light, no pedestrian way exists and the like;

a traffic question bank for describing traffic questions in a professional language, comprising:

1. traffic control type: unreasonable (more or less) time interval division, unreasonable (more or less) signal period setting, unreasonable (more or less) phase setting, unreasonable phase sequence setting, unreasonable signal timing and the like;

2. traffic organization type: the number of lanes is not matched, the lanes are too wide or too narrow, the lane function is unreasonable, the turning position is unreasonable, the size or the position of a safety island is unreasonable, | the sight distance of an intersection is insufficient, the intersection is not barrier-free and is not treated properly, the width of a non-motor vehicle/pedestrian lane is insufficient, the non-motor vehicle/pedestrian lane is not set, and the like;

3. traffic order class: motor vehicle order confusion, pedestrian order confusion, non-motor vehicle order confusion, motor-vehicle-to-non-mixed traffic facility: the signal lamp is unreasonable in arrangement, the mark is unreasonable in arrangement, the marking line is unreasonable in arrangement and the like;

the strategy recommendation library comprises a strategy recommendation library used for recommending a treatment strategy according to the traffic problem, and comprises the following steps:

1. traffic organization type: the system comprises a reverse variable lane, a tide lane, a left-turn waiting area, a straight-going waiting area, a comprehensive waiting area, a left-forbidden organization, a right-forbidden organization, a full-waiting traffic organization, a rotary control, a left-turn external organization, an intersection widening, a non-motor vehicle waiting area, a bus lane and the like;

2. signal control class: repeated release, double-period single-point optimization, induction control, semi-induction control, ramp control, bottleneck control, induction type green wave, coordination optimization, regional demand control, regional green wave and the like;

3. traffic order class: secondary crossing of pedestrians, coordinated crossing of pedestrians, special pedestrian phases and the like;

the national standard specification library comprises national standard specifications used for recommending association according to the disposal policy, and comprises:

GB 148862016 road traffic light setting and installation specification, GB 148872011 road traffic light, GAT 8512009 pedestrian crossing light setting specification, GB 252802016 road traffic signal control machine, GBT 366702018 urban road traffic organization design specification, GB57682009 road traffic sign and marking, GB 5103838383848 urban road traffic sign and marking setting specification, GB 506472011 urban road intersection planning specification, CJJC1522010 urban road intersection design specification, GA5272005 urban road traffic signal control mode applicable specification, CJI372012 urban road engineering design specification, DB11T 11632015 public transit lane setting specification, GB506882011 urban road traffic facility design specification, GAT 8502009 urban road in-road parking berth setting specification, GAT1152020 road traffic congestion evaluation method, GBT 3312016 urban traffic operation condition evaluation specification, and the like;

the application case base comprises a recommendation module for recommending associated application cases according to the disposal strategy, and the recommendation module comprises the following components:

reverse variable lane: yangji Lu Shandong road in Qingdao city; lane-changeable: yinchuan city Heilan mountain road-Tai Kang street; tidal lane: yanan three-way in Qingdao city; the traffic organization of waiting for all to walk: mountain Dadaodao Qinren road; and (4) forbidding left organization: and (3) controlling the vehicle roundabout in the east of Yangzhou coast: qingdao city haixin overpass; the non-motor vehicle waits for driving: the child road in Nanchang city is a road pile; bus priority: beijing Xilu in Nanchang; and (4) repeating releasing: guangzhou city is around-looking south; single-point optimization: the cigarette tai city happy road and happy middle road; single-point induction: the west road of double river-south road of tiger mountain in cigarette end; and (3) system optimization: heilan mountain road of Yinchuan city; and (3) demand control: jianghan district in Wuhan City; induction type green wave: yinchuan West Lu, Qingdao city; thermal imaging: great wall way in Yinchuan city; and (3) pedestrian coordination crossing: shanhai road of cigarette Tai city, etc.

The knowledge application layer provides knowledge service through knowledge retrieval and knowledge reasoning according to the query request of the user, and coordinates with knowledge of a traffic question bank, a strategy recommendation bank, a national standard bank, an application case bank and the like to make optimal strategy recommendation aiming at congestion problem representation. Meanwhile, the application success strategy can be used as a typical case to be supplemented to the knowledge matching layer, and the application value of the knowledge matching layer is fully exerted.

For convenience of understanding, fig. 3 is a flowchart of an implementation process of the embodiment of the present application, and the specific steps are as follows:

in step 301: acquiring a target traffic problem;

in one embodiment, the steps for obtaining the target traffic problem are as follows 1-2:

1. the method for acquiring the target traffic problem by adopting the bidirectional matching comprises the following specific implementation steps:

after the congestion phenomenon features are obtained, matching processing is carried out in a traffic question bank according to the congestion phenomenon features, and traffic questions matched with the congestion phenomenon features are obtained and serve as target traffic questions; the method is implemented in the following two ways:

mode 1:

as shown in fig. 4, in step 401: determining the matching degree between the congestion phenomenon characteristics and each candidate traffic problem in the traffic problem library through a matching degree calculation formula;

the matching degree calculation formula is as follows:

wherein the content of the first and second substances,

n is the number of candidate traffic problems, and each candidate problem corresponds to various congestion phenomenon characteristics; i denotes the ith traffic problem, x_ijA jth congestion feature representing an ith question, each of the traffic questions in the traffic question bank having a congestion characterization of y_i，y_i0 means that the congestion feature cannot be matched to a traffic problem in the traffic problem bank, y_i1 indicates that the congestion phenomenon feature can be matched to a traffic problem in the traffic problem bank,

an average of congestion features representing traffic problems; the commonly used evaluation indexes of the matching result are recall ratio RE and standardThe accuracy AP. The recall ratio reflects the comprehensiveness of the whole retrieval result, and refers to the percentage of the attribute related data to be retrieved and all related data in the database during retrieval, and whether the problem causing the phenomenon is retrieved according to the congestion phenomenon feature key words, and vice versa. The accuracy rate reflects the accuracy of the result to be retrieved, and refers to the percentage of the effective data and the data related to the attribute to be retrieved after the abnormal data are removed according to the screening rule.

The recall RE may be expressed as:

wherein: RT represents the related data of the attribute to be retrieved, and DB represents all the related data in the database;

the recall ratio in the embodiment of the application represents the percentage of the traffic problems searched out according to the congestion phenomenon characteristics in the traffic problem library.

The accuracy AP can be expressed as:

wherein: ST represents valid data after the abnormal data are removed;

in the embodiment of the application, the accuracy rate represents the percentage of the effective traffic problems searched out after the abnormal data are removed according to the congestion phenomenon characteristics in the traffic problem database.

When the knowledge system is established, the knowledge system can be obtained by training according to the recall ratio and the accuracy.

In step 402: sorting all the candidate traffic problems according to the matching degree from large to small;

in step 403: and selecting the matched traffic problem as the target traffic problem in each candidate traffic problem.

When each candidate traffic problem selects a matched traffic problem as a target traffic problem, the concrete implementation includes two schemes of A1 and A2 as follows:

it should be noted that the candidate traffic problem may be all the problems in the traffic problem library or may be a problem after performing preliminary screening on all the problems in the traffic problem library (all the problems in the traffic problem library may be screened according to the keywords), which is not limited in the embodiment of the present application;

a1 recommending n traffic problems to the user according to the sequence from big to small according to the matching degree on the display interface, wherein the user selects one of the n problems as a target traffic problem;

as shown in fig. 6, a2 sequentially recommends traffic problems with the highest matching degree to the user according to the matching degree, for example: recommending the traffic problem with the highest matching degree to the user, and taking the traffic problem as a target traffic problem if the user selects the traffic problem; and if the user does not select the traffic problem, the traffic problem is taken as a target traffic problem, and the problem with the highest matching degree in other traffic problems is recommended to the user.

Mode 2:

as shown in fig. 7, in step 701: obtaining the predetermined degree of correlation between the congestion phenomenon characteristics and a plurality of traffic problems;

in step 702: the plurality of traffic problems are ranked according to the relevancy, and a plurality of candidate traffic problems are output according to a ranking result;

the correlation is determined by a matching degree calculation formula, which is:

wherein n is the number of the plurality of traffic problems, and each traffic problem corresponds to a plurality of congestion phenomenon characteristics; i denotes the ith traffic problem, x_ijRepresenting a quantized value of the jth congestion phenomenon feature of the ith traffic problem, wherein the quantized value and the candidate traffic problem corresponding to the congestion feature have positive correlation with the click and recommendation times; the congestion phenomenon characteristic of each traffic question in the traffic question bank is y_i，y_i0 means that the congestion phenomenon feature cannot be matched to the traffic problem in the traffic problem bank, y_i1 indicates that the congestion phenomenon feature can be matched to the traffic problem in the traffic problem bank,

represents the average of the quantized values of the n traffic problems over the jth congestion phenomenon feature,

and the average value of the n traffic problems in all the congestion phenomenon characteristic values is represented.

In one embodiment, the specific implementation of the quantization value determination is as follows:

for example: a congestion phenomenon representation is matched with 3 traffic problems, the number of clicks and recommendations of the first traffic problem is 5, the number of clicks and recommendations of the second traffic problem is 3, the number of clicks and recommendations of the third traffic problem is 2,

x is then₁₁＝5/(5+3+2)＝0.5；x₁₂＝3/(5+3+2)＝0.3；x₁₃＝2/(5+3+2)＝0.2。

In step 703: and in response to the selection operation of the candidate traffic question, taking the selected candidate traffic question as a traffic question matched with the congestion phenomenon feature.

And if the correlation degree between the selected candidate traffic problem and the congestion phenomenon feature is not the maximum value in the plurality of candidate traffic problems, improving the correlation degree between the candidate traffic problem and the congestion phenomenon feature according to the number of clicks and recommendations of the candidate traffic problem.

In one embodiment, the arrangement order of the traffic problems calculated according to the matching degree is not fixed, and changes with the number of clicks and recommendations, so the arrangement order also changes. 2. Obtaining a target traffic problem according to the input of the user to the traffic problem;

in particular implementations, the inputs to the traffic problem may be: other input modes such as input box input, voice input, question option input and the like; the embodiment of the present application does not limit this.

In step 302: acquiring a target disposal strategy corresponding to the target traffic problem according to a corresponding relation between the traffic problem and the disposal strategy which is established in advance;

in step 303: searching a national standard and an application case associated with the target disposal strategy;

in step 304: and outputting and displaying the target disposal strategy and the associated national standard specification and application case thereof.

In one embodiment, as shown in fig. 8, after the matching process is performed in the traffic problem library according to the congestion phenomenon feature to obtain the traffic problem matched with the congestion phenomenon feature, the recommendation may be further optimized by expert experience, for example, the user may check the output result, and the following steps may be performed:

in step 801: as shown in fig. 9, a prompt for confirming whether or not the target traffic problem is adopted is output; if the data is adopted, the step 802 is entered, and if the data is not adopted, the step 803 is entered;

in step 802: detecting user operation for confirming adoption of the target traffic problem, and executing a step of acquiring a target disposal strategy corresponding to the target traffic problem according to a corresponding relation between the traffic problem and the disposal strategy which is established in advance;

in step 803: detecting a user operation confirming that the target traffic problem is not adopted, and performing any one of the following operations:

operation 1: acquiring new congestion problem characteristics, and returning to execute the step of matching processing in the traffic problem database according to the congestion phenomenon characteristics;

as shown in fig. 10, the new congestion problem feature may be manually entered or retrieved when it is acquired; the specific implementation is two schemes B1 and B2 shown as follows:

b1 was manually modified by an engineer who could enter new congestion problem features at the input box as shown in fig. 11;

b2 retrieves the text description information from the problem feature library and uses the text description information as a new congestion problem feature.

In an embodiment, based on the same pair concept, as shown in fig. 12, after the target handling policy corresponding to the target traffic problem is obtained according to the pre-established correspondence between the traffic problem and the handling policy, the recommended result may be further optimized by means of expert experience, and the following steps may be implemented:

in step 1201: outputting a prompt to confirm whether a target treatment strategy can alleviate the target traffic problem; if yes, go to step 1202, otherwise go to step 1203;

in step 1202: detecting user operation for confirming that the target disposal strategy can relieve the target traffic problem, and executing operation for searching national standard standards and application cases associated with the target disposal strategy;

in step 1203: detecting a user action confirming that the target handling strategy cannot alleviate the target traffic issue, performing any of the following:

operation 1: acquiring new congestion phenomenon characteristics, and returning to execute the step of matching processing in a traffic question bank according to the congestion phenomenon characteristics;

operation 2: and acquiring a new target handling strategy corresponding to the target traffic problem, and returning to execute and outputting a prompt for confirming whether the target handling strategy can relieve the target traffic problem.

In one embodiment, as shown in fig. 13, after performing the operation of finding the national standard specification and the application case associated with the target disposal policy, the following steps are implemented:

outputting a prompt for confirming whether the target handling policy is reasonable in step 1301, if so, entering step 1302, and if not, entering step 1303;

in step 1302: detecting user operation for confirming that the target handling strategy is reasonable, and executing operation for applying the target handling strategy;

in one embodiment, as shown in fig. 14, a target handling policy and an optimization policy input box are provided in the policy optimization interface, for example, a traffic light is added in a target road segment in the target handling policy, an engineer determines, according to the actual situation of the road segment, to add a traffic light at a specific position of the road segment, and debugs the road segment after adding the traffic light to obtain the optimization policy.

In step 1303: detecting a user action confirming that the target handling policy is unreasonable, performing any of the following:

In one embodiment, as shown in fig. 15, after the target disposal policy and its associated national standard specification and application case output are displayed, the following steps are implemented:

in step 1501: outputting a prompt for confirming whether the target traffic problem is resolved using the target disposition policy; if the solution can be solved, the process proceeds to step 1502, and if the solution cannot be solved, the process proceeds to step 1503;

in step 1502: detecting user operation for confirming that the target traffic problem can be solved, acquiring an input optimization strategy aiming at a target disposal strategy, and adding an incidence relation between the optimization strategy and the target traffic into a corresponding relation;

and when the target handling strategy corresponding to the target traffic problem is searched and obtained through the corresponding relation next time, preferentially recommending the target handling strategy and optimizing the strategy.

Each traffic problem in the policy recommendation library corresponds to a plurality of handling policies, each handling policy has a matching degree corresponding to the traffic problem, and the matching degree can be determined by the following modes:

1) recommended values are: as shown in fig. 16, the matching degrees between the respective disposal policies and the traffic problem are calculated, the matching degrees are arranged from large to small, and 1 or more disposal policies are recommended to the user in the order of the matching degrees;

2) number of applications or clicks: the handling policies are arranged by the number of applications according to the number of applications/clicks of each handling policy for the traffic problem, and 1 or more handling policies are recommended to the user according to the arrangement.

In step 1503: detecting a user operation confirming that the target traffic problem cannot be solved; performing any of the following operations:

For convenience of understanding, as shown in fig. 17, the following describes an overall flow of the traffic information processing method provided in the embodiment of the present application:

in step 1701: acquiring congestion phenomenon characteristics;

in step 1702: acquiring a target traffic problem according to the congestion phenomenon characteristics;

in step 1703: analyzing the target traffic problem, judging whether the traffic problem is adopted, if so, entering a step 1704, and if not, entering a step 1705;

in step 1704: acquiring a target disposal strategy corresponding to the target traffic problem;

in step 1705: judging whether new congestion problem features are acquired or not, if so, entering a step 1701, and if not, determining to enter

steps

1702 and 1704 according to specific steps;

in step 1706, it is determined whether the target handling policy can alleviate the target traffic problem, if yes, the process proceeds to step 1707, and if not, the process proceeds to step 1705;

in step 1707: judging whether the target disposal strategy is reasonable, if so, entering a step 1708, and if not, entering a step 1705;

in step 1708: outputting and displaying a target disposal strategy and related national standard and application case thereof;

in step 1709: it is determined whether the target traffic problem is resolved by the target handling policy, and if yes, the flow proceeds to step 1710, and if not, the flow proceeds to step 1705.

In step 1710: optimizing the target disposal strategy to obtain an optimization strategy;

in step 1711: debugging and implementing the optimization strategy;

in step 1712: and (6) completing optimization.

As shown in fig. 18, which is a schematic structural diagram of an apparatus according to an embodiment of the present application, the apparatus includes:

an obtaining module 18001, obtaining a target traffic problem;

a disposal policy obtaining module 18002, configured to obtain a target disposal policy corresponding to the target traffic problem according to a correspondence relationship between a traffic problem and a disposal policy that is established in advance;

a searching module 18003, configured to search a national standard and an application case associated with the target disposal policy;

a display module 18004 for displaying the target disposal policy and the national standard specification associated therewith and the application case output.

Having described a traffic information processing method and apparatus according to an exemplary embodiment of the present application, an electronic device according to another exemplary embodiment of the present application is described next.

As will be appreciated by one skilled in the art, aspects of the present application may be embodied as a system, method or program product. Accordingly, various aspects of the present application may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.

In some possible implementations, an electronic device according to the present application may include at least one processor, and at least one memory. Wherein the memory stores program code which, when executed by the processor, causes the processor to perform the steps of the traffic information processing method according to various exemplary embodiments of the present application described above in the present specification. The electronic apparatus 130 according to this embodiment of the present application is described below with reference to fig. 19. The electronic device 130 shown in fig. 19 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

As shown in fig. 19, the electronic apparatus 130 is represented in the form of a general electronic apparatus. The components of the electronic device 130 may include, but are not limited to: the at least one processor 131, the at least one memory 132, and a bus 133 that connects the various system components (including the memory 132 and the processor 131).

The memory 132 may include readable media in the form of volatile memory, such as Random Access Memory (RAM)1321 and/or cache memory 1322, and may further include Read Only Memory (ROM) 1323.

Memory 132 may also include a program/utility 1325 having a set (at least one) of program modules 1324, such program modules 1324 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

The electronic device 130 may also communicate with one or more external devices 134 (e.g., keyboard, pointing device, etc.), with one or more devices that enable a user to interact with the electronic device 130, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 130 to communicate with one or more other electronic devices. Such communication may occur via input/output (I/O) interfaces 135. Also, the electronic device 130 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 136. As shown, network adapter 136 communicates with other modules for electronic device 130 over bus 133. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 130, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

In some possible embodiments, various aspects of a traffic information processing method provided by the present disclosure may also be implemented in the form of a program product including program code for causing a computer device to perform the steps in the traffic information processing method according to various exemplary embodiments of the present disclosure described above in this specification when the program product is run on the computer device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

A program product for a traffic information processing method of an embodiment of the present disclosure may employ a portable compact disc read only memory (CD-ROM) and include program codes, and may be run on an electronic device. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the consumer electronic device, partly on the consumer electronic device, as a stand-alone software package, partly on the consumer electronic device and partly on a remote electronic device, or entirely on the remote electronic device or server. In the case of remote electronic devices, the remote electronic devices may be connected to the consumer electronic devices through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to external electronic devices (e.g., through the internet using an internet service provider).

It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, the features and functions of two or more units described above may be embodied in one unit, in accordance with embodiments of the present disclosure. Conversely, the features and functions of one unit described above may be further divided into embodiments by a plurality of units.

Further, while the operations of the disclosed methods are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

Claims

1. A traffic information processing method, characterized in that the method comprises:

acquiring a target traffic problem;

2. The method of claim 1, wherein the obtaining a target traffic problem comprises:

3. The method according to claim 2, wherein the performing matching processing in a traffic question bank according to the congestion phenomenon feature to obtain the traffic question matched with the congestion phenomenon feature comprises:

4. The method of claim 3, further comprising:

the matching degree calculation formula is as follows:

5. The method according to claim 3, wherein after the selecting the candidate traffic problem as the traffic problem matching the congestion phenomenon feature in response to the selecting operation of the candidate traffic problem, the method further comprises:

6. The method according to claim 2, wherein the performing matching processing in a traffic question bank according to the congestion phenomenon feature to obtain the traffic question matched with the congestion phenomenon feature comprises:

the matching degree calculation formula is as follows:

wherein n is the number of the plurality of traffic problems, and each traffic problem corresponds to a plurality of congestion phenomenon characteristics; i denotes the ith traffic problem, x_ijA quantized value representing the j congestion phenomenon feature of the ith traffic question, wherein the congestion phenomenon feature of each traffic question in the traffic question bank is y_i，y_i0 means that the congestion phenomenon feature cannot be matched to the traffic problem in the traffic problem bank, y_i1 indicates that the congestion phenomenon feature can be matched to the traffic problem in the traffic problem bank,

representing the average value of the n traffic problems in all the congestion phenomenon characteristic values;

7. The method according to claim 2, wherein after the matching process is performed in a traffic question bank according to the congestion phenomenon feature to obtain the traffic question matched with the congestion phenomenon feature, the method further comprises:

outputting a prompt for confirming whether to adopt the target traffic problem;

8. The method according to claim 1, wherein after obtaining a target disposal policy corresponding to the target traffic problem according to a pre-established correspondence between the traffic problem and the disposal policy, the method further comprises:

9. The method as claimed in claim 8, wherein after the performing the operation of finding the national standard specification and application case associated with the target disposition policy, the method further comprises:

10. An electronic device for processing traffic information, the electronic device comprising a processor and a memory:

the memory for storing a computer program executable by the processor;

the processor is coupled to the memory and configured to:

acquiring a target traffic problem;